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wyzely-detect
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Merge pull request #4 from slashtechno/deepface 

Add facial recognition
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slashtechno 2023-10-14 17:39:49 -05:00 committed by GitHub
parent 556a90da3c b2f10280b3
commit 7d942ee456
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13 changed files with 1690 additions and 335 deletions
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4
.gitignore vendored
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@ -2,5 +2,7 @@
config/
using_yolov8.ipynb
yolov8n.pt
.venv/
__pycache__/
faces/*
!faces/.gitkeep

2
.vscode/launch.json vendored
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@ -8,7 +8,7 @@
"name": "Python: Module",
"type": "python",
"request": "launch",
"module": "src",
"module": "set_detect_notify",
"justMyCode": true
}
]

95
deepface-test.ipynb Normal file
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@ -0,0 +1,95 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from deepface import DeepFace\n",
"import cv2\n",
"from pathlib import Path\n",
"import uuid\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Take pictures"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Take a picture using opencv with <uuid>.jpg\n",
"# Then delete it after\n",
"cap = cv2.VideoCapture(0)\n",
"ret, frame = cap.read()\n",
"cap.release()\n",
"# uuid_str = str(uuid.uuid4())\n",
"# uuid_path = Path(uuid_str + \".jpg\")\n",
"# cv2.imwrite(str(uuid_path), frame)\n",
"# dfs = DeepFace.find(img_path=str(uuid_path), db_path = \"faces\")\n",
"# Don't throw an error if no face is detected (enforce_detection=False)\n",
"dfs = DeepFace.find(frame, db_path = \"faces\", enforce_detection=False)\n",
"# Get the identity of the person\n",
"for i, pd_dataframe in enumerate(dfs):\n",
" # Sort the dataframe by confidence\n",
" # inplace=True means that the dataframe is modified so we don't need to assign it to a new variable\n",
" pd_dataframe.sort_values(by=['VGG-Face_cosine'], inplace=True, ascending=False)\n",
" print(f'On dataframe {i}')\n",
" print(pd_dataframe)\n",
" # Get the most likely identity\n",
" # print(f'Most likely identity: {pd_dataframe.iloc[0][\"identity\"]}')\n",
" # We could use Path to get the parent directory of the image to use as the identity\n",
" print(f'Most likely identity: {Path(pd_dataframe.iloc[0][\"identity\"]).parent.name}')\n",
" # Get the most likely identity's confidence\n",
" print(f'Confidence: {pd_dataframe.iloc[0][\"VGG-Face_cosine\"]}')\n",
"\n",
"# uuid_path.unlink()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Stream"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"DeepFace.stream(db_path=\"faces\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

0
set-detect-notify/__init__.py → faces/.gitkeep
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1365
poetry.lock generated
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49
pyproject.toml
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@ -1,58 +1,36 @@
[tool.poetry]
name = "set-detect-notify"
name = "set_detect_notify"
version = "0.1.0"
description = "Detect all the things"
authors = ["slashtechno <77907286+slashtechno@users.noreply.github.com>"]
license = "MIT"
readme = "README.md"
packages = [{include = "set-detect-notify"}]
packages = [{include = "set_detect_notify"}]
[tool.poetry.dependencies]
python = "^3.10"
# python = "^3.10"
python = ">=3.10, <3.12"
python-dotenv = "^1.0.0"
httpx = "^0.25.0"
opencv-python = "^4.8.1.78"
ultralytics = "^8.0.190"
hjson = "^3.1.0"
numpy = "^1.23.2"
# torch = [
# { version = "^2.0.0+cu118", source = "torch_cu118", markers = "extra=='cuda'" },
# { version = "^2.0.0+cpu", source = "torch_cpu", markers = "extra!='cuda'" },
# ]
# torchaudio = [
# { version = "^2.0.0+cu118", source = "torch_cu118", markers = "extra=='cuda'" },
# { version = "^2.0.0+cpu", source = "torch_cpu", markers = "extra!='cuda'" },
# ]
# torchvision = [
# { version = "^0.15+cu118", source = "torch_cu118", markers = "extra=='cuda'" },
# { version = "^0.15+cpu", source = "torch_cpu", markers = "extra!='cuda'" },
# ]
# https://github.com/python-poetry/poetry/issues/6409
torch = "^2.1.0"
tensorflow-io-gcs-filesystem = "0.31.0"
deepface = "^0.0.79"
[tool.poetry.group.dev.dependencies]
black = "^23.9.1"
ruff = "^0.0.291"
ipykernel = "^6.25.2"
nbconvert = "^7.9.2"
# [[tool.poetry.source]]
# name = "torch_cpu"
# url = "https://download.pytorch.org/whl/cpu"
# priority = "supplemental"
#
# [[tool.poetry.source]]
# name = "torch_cu118"
# url = "https://download.pytorch.org/whl/cu118"
# priority = "supplemental"
#
# [tool.poetry.extras]
# cuda = []
#
# [[tool.poetry.source]]
# name = "PyPI"
# priority = "primary"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
@ -60,4 +38,9 @@ build-backend = "poetry.core.masonry.api"
[tool.ruff]
# More than the default (88) of `black` to make comments less of a headache
line-length = 120
# Where possible, `black` will attempt to format to 88 characters
# However, setting ruff to 135 will allow for longer lines that can't be auto-formatted
line-length = 135
[tool.poetry.scripts]
set-detect-notify = "set_detect_notify.__main__:main"

32
set-detect-notify/utils/notify.py
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@ -1,32 +0,0 @@
import datetime
import httpx
def construct_ntfy_headers(
title: str = "Object/Person Detected",
tag="rotating_light", # https://docs.ntfy.sh/publish/#tags-emojis
priority="default", # https://docs.ntfy.sh/publish/#message-priority
) -> dict:
return {"Title": title, "Priority": priority, "Tags": tag}
def send_notification(data: str, headers: dict, url: str):
if url is None or data is None:
raise ValueError("url and data cannot be None")
httpx.post(url, data=data.encode("utf-8"), headers=headers)
def check_last_seen(last_seen: datetime.datetime, seconds: int = 15):
"""
Check if a time is older than a given number of seconds
If it is, return True
If last_seen is empty/null, return True
"""
if (
datetime.datetime.now() - last_seen > datetime.timedelta(seconds=seconds)
or last_seen == ""
or last_seen is None
):
return True
else:
return False

57
set-detect-notify/utils/utils.py
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@ -1,57 +0,0 @@
import cv2
import numpy as np
def plot_label(
# list of dicts with each dict containing a label, x1, y1, x2, y2
boxes: list = None,
# opencv image
full_frame: np.ndarray = None,
# run_scale is the scale of the image that was used to run the model
# So the coordinates will be scaled up to the view frame size
run_scale: float = None,
# view_scale is the scale of the image, in relation to the full frame
# So the coordinates will be scaled appropriately when coming from run_frame
view_scale: float = None,
font: int = cv2.FONT_HERSHEY_SIMPLEX,
):
view_frame = cv2.resize(full_frame, (0, 0), fx=view_scale, fy=view_scale)
for thing in boxes:
cv2.rectangle(
# Image
view_frame,
# Start point
(
int(thing["x1"] * (run_scale / view_scale)),
int(thing["y1"] * (run_scale / view_scale)),
),
# End point
(
int(thing["x2"] * (run_scale / view_scale)),
int(thing["y2"] * (run_scale / view_scale)),
),
# Color
(0, 255, 0),
# Thickness
2,
)
cv2.putText(
# Image
view_frame,
# Text
thing["label"],
# Origin
(
int(thing["x1"] * (run_scale / view_scale)),
int(thing["y1"] * (run_scale / view_scale)),
),
# Font
font,
# Font Scale
1,
# Color
(0, 255, 0),
# Thickness
1,
)
return view_frame

0
set-detect-notify/utils/__init__.py → set_detect_notify/__init__.py
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143
set-detect-notify/__main__.py → set_detect_notify/__main__.py
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@ -1,10 +1,8 @@
# import face_recognition
import cv2
import numpy as np
import dotenv
from pathlib import Path
import os
import time
# import hjson as json
import torch
@ -18,11 +16,14 @@ from .utils import utils
DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S"
args = None
object_names = {}
objects_and_peoples = {
"objects": {},
"peoples": {},
}
def main():
global object_names
global objects_and_peoples
global args
# RUN_BY_COMPOSE = os.getenv("RUN_BY_COMPOSE") # Replace this with code to check for gpu
@ -45,7 +46,8 @@ def main():
# Set it to the env RUN_SCALE if it isn't blank, otherwise set it to 0.25
default=os.environ["RUN_SCALE"]
if "RUN_SCALE" in os.environ and os.environ["RUN_SCALE"] != ""
else 0.25, # noqa: E501
# else 0.25,
else 1,
type=float,
help="The scale to run the detection at, default is 0.25",
)
@ -54,7 +56,8 @@ def main():
# Set it to the env VIEW_SCALE if it isn't blank, otherwise set it to 0.75
default=os.environ["VIEW_SCALE"]
if "VIEW_SCALE" in os.environ and os.environ["VIEW_SCALE"] != ""
else 0.75, # noqa: E501
# else 0.75,
else 1,
type=float,
help="The scale to view the detection at, default is 0.75",
)
@ -77,6 +80,15 @@ def main():
help="The object(s) to detect. Must be something the model is trained to detect",
)
argparser.add_argument(
"--faces-directory",
default=os.environ["FACES_DIRECTORY"]
if "FACES_DIRECTORY" in os.environ and os.environ["FACES_DIRECTORY"] != ""
else "faces",
type=str,
help="The directory to store the faces. Should contain 1 subdirectory of images per person",
)
stream_source = argparser.add_mutually_exclusive_group()
stream_source.add_argument(
"--url",
@ -95,6 +107,10 @@ def main():
help="The capture device to use. Can also be a url.",
)
# Defaults for the stuff here and down are already set in notify.py.
# Setting them here just means that argparse will display the default values as defualt
# TODO: Perhaps just remove the default parameter and just add to the help message that the default is set is x
notifcation_services = argparser.add_argument_group("Notification Services")
notifcation_services.add_argument(
"--ntfy-url",
@ -177,19 +193,39 @@ def main():
for i, r in enumerate(results):
# list of dicts with each dict containing a label, x1, y1, x2, y2
plot_boxes = []
# The following is stuff for people
# This is still in the for loop as each result, no matter if anything is detected, will be present.
# Thus, there will always be one result (r)
if face_details := utils.recognize_face(path_to_directory=Path(args.faces_directory), run_frame=run_frame):
plot_boxes.append( face_details )
objects_and_peoples=notify.thing_detected(
thing_name=face_details["label"],
objects_and_peoples=objects_and_peoples,
detection_type="peoples",
detection_window=args.detection_window,
detection_duration=args.detection_duration,
notification_window=args.notification_window,
ntfy_url=args.ntfy_url,
)
# The following is stuff for objects
# Setup dictionary of object names
if not object_names:
if objects_and_peoples["objects"] == {} or objects_and_peoples["objects"] is None:
for name in r.names.values():
object_names[name] = {
objects_and_peoples["objects"][name] = {
"last_detection_time": None,
"detection_duration": None,
# "first_detection_time": None,
"last_notification_time": None,
}
# Also, make sure that the objects to detect are in the list of object_names
# Also, make sure that the objects to detect are in the list of objects_and_peoples
# If it isn't, print a warning
for obj in args.detect_object:
if obj not in object_names:
if obj not in objects_and_peoples:
print(
f"Warning: {obj} is not in the list of objects the model can detect!"
)
@ -228,79 +264,18 @@ def main():
}
)
# End goal: Send a notification when an object has been detected for 2 seconds in the past 15 seconds.
# However, don't send a notification if the last notification was less than 15 seconds ago
objects_and_peoples=notify.thing_detected(
thing_name=class_id,
objects_and_peoples=objects_and_peoples,
detection_type="objects",
detection_window=args.detection_window,
detection_duration=args.detection_duration,
notification_window=args.notification_window,
ntfy_url=args.ntfy_url,
)
# (re)start cycle
if (
# If the object has not been detected before
object_names[class_id]["last_detection_time"] is None
# If the last detection was more than 15 seconds ago
or time.time() - object_names[class_id]["last_detection_time"]
> args.detection_window
):
# Set the last detection time to now
object_names[class_id]["last_detection_time"] = time.time()
print(f"First detection of {class_id} in this detection window")
# This line is important. It resets the detection duration when the object hasn't been detected for a while
# If detection duration is None, don't print anything.
# Otherwise, print that the detection duration is being reset due to inactivity
if object_names[class_id]["detection_duration"] is not None:
print(
f"Resetting detection duration for {class_id} since it hasn't been detected for {args.detection_window} seconds" # noqa: E501
)
object_names[class_id]["detection_duration"] = 0
else:
# Check if the last notification was less than 15 seconds ago
# If it was, then don't do anything
if (
time.time() - object_names[class_id]["last_detection_time"]
<= args.notification_window
):
pass
# If it was more than 15 seconds ago, reset the detection duration
# This effectively resets the notification timer
else:
print("Notification timer has expired - resetting")
object_names[class_id]["detection_duration"] = 0
object_names[class_id]["detection_duration"] += (
time.time() - object_names[class_id]["last_detection_time"]
)
# print("Updating detection duration")
object_names[class_id]["last_detection_time"] = time.time()
# (re)send notification
# Check if detection has been ongoing for 2 seconds or more in the past 15 seconds
if (
object_names[class_id]["detection_duration"]
>= args.detection_duration
and time.time() - object_names[class_id]["last_detection_time"]
<= args.detection_window
):
# If the last notification was more than 15 seconds ago, then send a notification
if (
object_names[class_id]["last_notification_time"] is None
or time.time()
- object_names[class_id]["last_notification_time"]
> args.notification_window
):
object_names[class_id]["last_notification_time"] = time.time()
print(
f"Detected {class_id} for {args.detection_duration} seconds"
)
headers = notify.construct_ntfy_headers(
title=f"{class_id} detected",
tag="rotating_light",
priority="default",
)
notify.send_notification(
data=f"{class_id} detected for {args.detection_duration} seconds",
headers=headers,
url=args.ntfy_url,
)
# Reset the detection duration
print("Just sent a notification - resetting detection duration")
object_names[class_id]["detection_duration"] = 0
# TODO: On 10-14-2023, while testing, it seemed the bounding box was too low. Troubleshoot if it's a plotting problem.
# To do so, use r.plot() to cross reference the bounding box drawn by the plot_label function and r.plot()
frame_to_show = utils.plot_label(
boxes=plot_boxes,
full_frame=frame,
@ -322,5 +297,5 @@ def main():
video_capture.release()
cv2.destroyAllWindows()
main()
if __name__ == "__main__":
main()

0
set_detect_notify/utils/__init__.py Normal file
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143
set_detect_notify/utils/notify.py Normal file
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@ -0,0 +1,143 @@
import httpx
import time
'''
Structure of objects_and_peoples
Really, the only reason peoples is a separate dictionary is to prevent duplicates, though it just makes the code more complicated.
TODO: Make a function to check if a person is in the objects dictionary and vice versa
{
"objects": {
"object_name": {
"last_detection_time": float,
"detection_duration": float,
"last_notification_time": float,
},
},
"peoples": {
"person_name": {
"last_detection_time": float,
"detection_duration": float,
"last_notification_time": float,
},
},
}
'''
# objects_and_peoples = {}
def thing_detected(
thing_name: str,
objects_and_peoples: dict,
detection_type: str = "objects",
detection_window: int = 15,
detection_duration: int = 2,
notification_window: int = 15,
ntfy_url: str = "https://ntfy.sh/set-detect-notify"
) -> dict:
'''
A function to make sure 2 seconds of detection is detected in 15 seconds, 15 seconds apart.
Takes a dict that will be retured with the updated detection times. MAKE SURE TO SAVE THE RETURNED DICTIONARY
'''
# "Alias" the objects and peoples dictionaries so it's easier to work with
respective_type = objects_and_peoples[detection_type]
# (re)start cycle
try:
if (
# If the object has not been detected before
respective_type[thing_name]["last_detection_time"] is None
# If the last detection was more than 15 seconds ago
or time.time() - respective_type[thing_name]["last_detection_time"]
> detection_window
):
# Set the last detection time to now
respective_type[thing_name]["last_detection_time"] = time.time()
print(f"First detection of {thing_name} in this detection window")
# This line is important. It resets the detection duration when the object hasn't been detected for a while
# If detection duration is None, don't print anything.
# Otherwise, print that the detection duration is being reset due to inactivity
if respective_type[thing_name]["detection_duration"] is not None:
print(
f"Resetting detection duration for {thing_name} since it hasn't been detected for {detection_window} seconds" # noqa: E501
)
respective_type[thing_name]["detection_duration"] = 0
else:
# Check if the last NOTIFICATION was less than 15 seconds ago
# If it was, then don't do anything
if (
time.time() - respective_type[thing_name]["last_detection_time"]
<= notification_window
):
pass
# If it was more than 15 seconds ago, reset the detection duration
# This effectively resets the notification timer
else:
print("Notification timer has expired - resetting")
respective_type[thing_name]["detection_duration"] = 0
respective_type[thing_name]["detection_duration"] += (
time.time() - respective_type[thing_name]["last_detection_time"]
)
# print("Updating detection duration")
respective_type[thing_name]["last_detection_time"] = time.time()
except KeyError:
# If the object has not been detected before
respective_type[thing_name] = {
"last_detection_time": time.time(),
"detection_duration": 0,
"last_notification_time": None,
}
print(f"First detection of {thing_name} ever")
# (re)send notification
# Check if detection has been ongoing for 2 seconds or more in the past 15 seconds
if (
respective_type[thing_name]["detection_duration"]
>= detection_duration
and time.time() - respective_type[thing_name]["last_detection_time"]
<= detection_window
):
# If the last notification was more than 15 seconds ago, then send a notification
if (
respective_type[thing_name]["last_notification_time"] is None
or time.time()
- respective_type[thing_name]["last_notification_time"]
> notification_window
):
respective_type[thing_name]["last_notification_time"] = time.time()
print(
f"Detected {thing_name} for {detection_duration} seconds"
)
headers = construct_ntfy_headers(
title=f"{thing_name} detected",
tag="rotating_light",
priority="default",
)
send_notification(
data=f"{thing_name} detected for {detection_duration} seconds",
headers=headers,
url=ntfy_url,
)
# Reset the detection duration
print("Just sent a notification - resetting detection duration")
respective_type[thing_name]["detection_duration"] = 0
# Take the aliased objects_and_peoples and update the respective dictionary
objects_and_peoples[detection_type] = respective_type
return objects_and_peoples
def construct_ntfy_headers(
title: str = "Object/Person Detected",
tag="rotating_light", # https://docs.ntfy.sh/publish/#tags-emojis
priority="default", # https://docs.ntfy.sh/publish/#message-priority
) -> dict:
return {"Title": title, "Priority": priority, "Tags": tag}
def send_notification(data: str, headers: dict, url: str):
if url is None or data is None:
raise ValueError("url and data cannot be None")
httpx.post(url, data=data.encode("utf-8"), headers=headers)

133
set_detect_notify/utils/utils.py Normal file
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@ -0,0 +1,133 @@
import cv2
import numpy as np
from pathlib import Path
from deepface import DeepFace
first_face_try = True
def plot_label(
# list of dicts with each dict containing a label, x1, y1, x2, y2
boxes: list = None,
# opencv image
full_frame: np.ndarray = None,
# run_scale is the scale of the image that was used to run the model
# So the coordinates will be scaled up to the view frame size
run_scale: float = None,
# view_scale is the scale of the image, in relation to the full frame
# So the coordinates will be scaled appropriately when coming from run_frame
view_scale: float = None,
font: int = cv2.FONT_HERSHEY_SIMPLEX,
):
view_frame = cv2.resize(full_frame, (0, 0), fx=view_scale, fy=view_scale)
for thing in boxes:
cv2.rectangle(
# Image
view_frame,
# Start point
(
int(thing["x1"] * (run_scale / view_scale)),
int(thing["y1"] * (run_scale / view_scale)),
),
# End point
(
int(thing["x2"] * (run_scale / view_scale)),
int(thing["y2"] * (run_scale / view_scale)),
),
# Color
(0, 255, 0),
# Thickness
2,
)
cv2.putText(
# Image
view_frame,
# Text
thing["label"],
# Origin
(
int(thing["x1"] * (run_scale / view_scale)),
int(thing["y1"] * (run_scale / view_scale)),
),
# Font
font,
# Font Scale
1,
# Color
(0, 255, 0),
# Thickness
1,
)
return view_frame
def recognize_face(
path_to_directory: Path = Path("faces"),
# opencv image
run_frame: np.ndarray = None,
) -> np.ndarray:
'''
Accepts a path to a directory of images of faces to be used as a refference
In addition, accepts an opencv image to be used as the frame to be searched
Returns a single dictonary as currently only 1 face can be detected in each frame
dict contains the following keys: label, x1, y1, x2, y2
The directory should be structured as follows:
faces/
name/
image1.jpg
image2.jpg
image3.jpg
name2/
image1.jpg
image2.jpg
image3.jpg
(not neccessarily jpgs, but you get the idea)
Point is, `name` is the name of the person in the images in the directory `name`
That name will be used as the label for the face in the frame
'''
global first_face_try
# If it's the first time the function is being run, remove representations_vgg_face.pkl, if it exists
if first_face_try:
try:
Path("representations_vgg_face.pkl").unlink()
print("Removing representations_vgg_face.pkl")
except FileNotFoundError:
pass
first_face_try = False
# face_dataframes is a vanilla list of dataframes
try:
face_dataframes = DeepFace.find(run_frame, db_path=str(path_to_directory), enforce_detection=True, silent=True)
except ValueError as e:
if str(e) == "Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.":
return None
# Iteate over the dataframes
for df in face_dataframes:
# The last row is the highest confidence
# So we can just grab the path from there
# iloc = Integer LOCation
path_to_image = Path(df.iloc[-1]["identity"])
# Get the name of the parent directory
label = path_to_image.parent.name
# Return the coordinates of the box in xyxy format, rather than xywh
# This is because YOLO uses xyxy, and that's how plot_label expects
# Also, xyxy is just the top left and bottom right corners of the box
coordinates = {
"x1": df.iloc[-1]["source_x"],
"y1": df.iloc[-1]["source_y"],
"x2": df.iloc[-1]["source_x"] + df.iloc[-1]["source_w"],
"y2": df.iloc[-1]["source_y"] + df.iloc[-1]["source_h"],
}
distance = df.iloc[-1]["VGG-Face_cosine"]
# if 0.5 < distance < 0.7:
# label = "Unknown"
to_return = dict(label=label, **coordinates)
print(f'Confindence: {distance}, filname: {path_to_image.name}, to_return: {to_return}')
return to_return
'''
Example dataframe, for reference
identity (path to image) | source_x | source_y | source_w | source_h | VGG-Face_cosine (pretty much the confidence \_('_')_/)
'''